{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "code_folding": [],
    "hidden_ranges": [],
    "id": "dWX9oFUhBRqF",
    "originalKey": "451ca1f5-6986-4c8b-97f7-f6b39a57c8e7",
    "showInput": false
   },
   "source": [
    "# Tutorial: Bayesian Structural Time Series Model\n",
    "This tutorial demonstrates modeling and running inference on various Bayesian Structural Time Series (STS) models. We demonstrate some great features of Bean Machine such as: modeling serially correlated variables, the NUTS sampler applied to a global pool of variables, and providing custom distributions for inference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import os\n",
    "\n",
    "if 'google.colab' in sys.modules and 'beanmachine' not in sys.modules:\n",
    "    !pip install beanmachine\n",
    "\n",
    "smoke_test = ('SANDCASTLE_NEXUS' in os.environ or 'CI' in os.environ)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "code_folding": [],
    "hidden_ranges": [],
    "id": "V1vcgcWmCoKz",
    "originalKey": "12e9ea9e-2e43-4bd7-8904-2fd89d0ab7e8",
    "showInput": false
   },
   "source": [
    "# Problem\n",
    "[Bayesian STS](https://en.wikipedia.org/wiki/Bayesian_structural_time_series) is a general class of additive models for *Time Series* series data;\n",
    "$y_1, y_2, ..., y_n$ that have an associated *State Space* denoted by; $\\alpha_1, \\alpha_2, ..., \\alpha_n$ which provides a probabilistic model for the serial correlation observed in the time series. For this tutorial, we will consider the case when $\\alpha_i$ takes values in a continous state space (For a discussion of the discrete state space case, refer to this tutorial on [Hidden Markov Models](https://github.com/facebookresearch/beanmachine/blob/main/tutorials/Hidden_Markov_model.ipynb)).\n",
    "\n",
    "Bayesian STS models typically contain, but are not limited to, some additive mixture of *Error* ($\\epsilon$), *Trend* ($\\mu$), and *Seasonality* ($\\gamma$) components:\n",
    "$$y_t = \\mu_t + \\gamma_t + \\epsilon_t$$\n",
    "Where each component is controlled either by some recursive relation of the state space, $\\alpha_i$ or external covariates (i.e. Regression) such as day-of-week effects, business logic, or categories of the data.\n",
    "When STS just contains just the Error, Trend, and Seasonality components, it is sometimes referred to as the ETS model. Possibly the simpliest version of STS is called the *local level* model which is where we will start our discussion after some prerequisites."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4E_-QiKcWZ3c",
    "originalKey": "da6f6ad2-a13b-405e-b8a3-b88948e35d26",
    "showInput": false
   },
   "source": [
    "# Prerequisites\n",
    "Let's code this in Bean Machine! Import the Bean Machine library, some fundamental PyTorch classes, and optionally typing for our code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "from typing import List\n",
    "\n",
    "import beanmachine.ppl as bm\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import torch\n",
    "import torch.distributions as dist\n",
    "from beanmachine.ppl.model import RVIdentifier\n",
    "\n",
    "\n",
    "logging.getLogger(\"beanmachine\").setLevel(50)\n",
    "bm.seed(111)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "code_folding": [],
    "hidden_ranges": [],
    "id": "8B1hDVLPGYRD",
    "originalKey": "2c02835f-1a63-47cd-a361-4632ad5b7347",
    "showInput": false
   },
   "source": [
    "# Local Level Model\n",
    "In this simple *local level* version of an STS, $y_t$ is the sum of our state variable $\\alpha_t$ plus Gaussian Noise, $\\epsilon_t$. All of the time dependency is modeled in the evolution of $\\alpha_{t+1}$, which is equal to its previous time value $\\alpha_t$ plus Gaussian Noise, $\\eta_t$ that is independent of all $\\epsilon_i$'s. This is sometimes referred to a *random walk* because at each step, noise is injected to the previous state to arrive at a new state.\n",
    "To summarize, the random variables in play at this point are:\n",
    "*  $\\epsilon_t \\stackrel{iid}{\\sim} \\text{Normal}(0, \\sigma_\\epsilon^2)$\n",
    "*   $\\eta_t \\stackrel{iid}{\\sim} \\text{Normal}(0, \\sigma_\\eta^2)$\n",
    "* $\\eta_t \\perp \\epsilon_i, \\forall i \\in \\{1,..., n\\}$\n",
    "\n",
    "Most STS formulations are expressed in the [state space](https://en.wikipedia.org/wiki/State-space_representation) representation, which is a coupled set of equations known as the *measurement* and *state* equations. Using our notation from before, the *local level* state space representation is:\n",
    "\n",
    "| Name | Formula\n",
    "| --- | ---\n",
    "| Measurement Equation | $y_t =  \\alpha_t + \\epsilon_t$\n",
    "| State Equation | $\\alpha_{t+1} = \\alpha_t + \\eta_t$  \n",
    "\n",
    "This can be represented more compactly as:\n",
    "* $\\alpha_{t+1} | \\alpha_t \\sim \\text{Normal}(\\alpha_{t}, \\sigma_\\eta^2)$\n",
    "* $y_t | \\alpha_t \\sim \\text{Normal}(\\alpha_t, \\sigma_e^2)$\n",
    "\n",
    "\n",
    "To make our STS \"Bayesian\", we will assign priors to $\\sigma_\\epsilon$, $\\sigma_\\eta$, and our initial state $\\alpha_1$:\n",
    "* $\\sigma_\\epsilon \\sim \\text{Gamma}(\\epsilon_{\\text{shape}}, \\epsilon_{\\text{rate}})$\n",
    "* $\\sigma_\\eta \\sim \\text{Gamma}(\\eta_{\\text{shape}}, \\eta_{\\text{rate}})$\n",
    "* $\\alpha_1 \\sim \\text{Normal}(a_1, p_1^2)$\n",
    "\n",
    "We can implement this model in Bean Machine by defining random variable objects with the `@bm.random_variable` decorator. These functions behave differently than ordinary Python functions.\n",
    "\n",
    "Semantics for `@bm.random_variable` functions:\n",
    "* They must return PyTorch `Distribution` objects.\n",
    "* Though they return distributions, callees actually receive samples from the distribution. The machinery for obtaining samples from distributions is handled internally by Bean Machine.\n",
    "* Inference runs the model through many iterations. During a particular inference iteration, a distinct random variable will correspond to exactly one sampled value: **calls to the same random variable function with the same arguments will receive the same sampled value within one inference iteration**. This makes it easy for multiple components of your model to refer to the same logical random variable.\n",
    "* Consequently, to define distinct random variables that correspond to different sampled values during a particular inference iteration, an effective practice is to add a dummy \"indexing\" parameter to the function. Distinct random variables can be referred to with different values for this index.\n",
    "* Please see the documentation for more information about this decorator.\n",
    "\n",
    "Note also that, compared to the statistical notation above, our implementation uses 0-indexing instead of 1-indexing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LocalLevelModel:\n",
    "    def __init__(\n",
    "        self,\n",
    "        N: int,\n",
    "        epsilon_shape: float,\n",
    "        epsilon_rate: float,\n",
    "        eta_shape: float,\n",
    "        eta_rate: float,\n",
    "        a_1: float,\n",
    "        p_1: float,\n",
    "    ) -> None:\n",
    "        self.N = N\n",
    "        self.epsilon_shape = epsilon_shape\n",
    "        self.epsilon_rate = epsilon_rate\n",
    "        self.eta_shape = eta_shape\n",
    "        self.eta_rate = eta_rate\n",
    "        self.a_1 = a_1\n",
    "        self.p_1 = p_1\n",
    "\n",
    "    @bm.random_variable\n",
    "    def SigmaEpsilon(self) -> RVIdentifier:\n",
    "        return dist.Gamma(self.epsilon_shape, self.epsilon_rate)\n",
    "\n",
    "    @bm.random_variable\n",
    "    def SigmaEta(self) -> RVIdentifier:\n",
    "        return dist.Gamma(self.eta_shape, self.eta_rate)\n",
    "\n",
    "    @bm.random_variable\n",
    "    def Alpha(self, n: int) -> RVIdentifier:\n",
    "        if n == 0:\n",
    "            return dist.Normal(self.a_1, self.p_1)\n",
    "        else:\n",
    "            return dist.Normal(self.Alpha(n - 1), self.SigmaEta())\n",
    "\n",
    "    @bm.random_variable\n",
    "    def Y(self, n: int) -> RVIdentifier:\n",
    "        return dist.Normal(self.Alpha(n), self.SigmaEpsilon())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "code_folding": [],
    "hidden_ranges": [],
    "id": "ZF07vzGPdMDy",
    "originalKey": "2b075396-2a1d-45e5-b911-2918b16087e3",
    "showInput": false
   },
   "source": [
    "## Data\n",
    "`iclaims` is a dataset containing the weekly initial claims for US unemployment benefits against a few related google trend queries (unemploy, filling and job)from Jan 2010 - June 2018.\n",
    "This dataset was curated [here](https://github.com/uber/orbit/blob/dev/orbit/utils/dataset.py#L5) and credit goes towards Uber's Orbit package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_iclaims(end_date='2018-06-24', transform=True):\n",
    "    \"\"\"Load iclaims dataset\n",
    "    Returns\n",
    "    -------\n",
    "        pd.DataFrame\n",
    "    Notes\n",
    "    -----\n",
    "    iclaims is a dataset containing the weekly initial claims for US unemployment benefits against a few related google\n",
    "    trend queries (unemploy, filling and job)from Jan 2010 - June 2018. This aims to mimick the dataset from the paper\n",
    "    Predicting the Present with Bayesian Structural Time Series by SCOTT and VARIAN (2014).\n",
    "    Number of claims are obtained from [Federal Reserve Bank of St. Louis] while google queries are obtained through\n",
    "    Google Trends API.\n",
    "    Note that dataset is transformed by natural log before fitting in order to be fitted as a multiplicative model.\n",
    "    https://fred.stlouisfed.org/series/ICNSA\n",
    "    https://trends.google.com/trends/?geo=US\n",
    "    https://finance.yahoo.com/\n",
    "    \"\"\"\n",
    "    url = 'https://raw.githubusercontent.com/uber/orbit/master/examples/data/iclaims_example.csv'\n",
    "    df = pd.read_csv(url, parse_dates=['week'])\n",
    "    df = df[df['week'] <= end_date]\n",
    "\n",
    "    # standardize the regressors by mean; equivalent to subtracting mean after np.log\n",
    "    regressors = ['trend.unemploy', 'trend.filling', 'trend.job', 'sp500', 'vix']\n",
    "\n",
    "    # convert to float\n",
    "    for col in regressors:\n",
    "        df[col] = df[col].astype(float)\n",
    "\n",
    "    if transform:\n",
    "        # log transfer\n",
    "        df[['claims'] + regressors] = df[['claims'] + regressors].apply(np.log)\n",
    "        # de-mean\n",
    "        df[regressors] = df[regressors] - df[regressors].apply(np.mean)\n",
    "\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_obs = torch.tensor(load_iclaims().claims.values)[:40]\n",
    "plt.plot(y_obs)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LHVb0PEadJg2",
    "originalKey": "996e6153-c42e-47f7-bf15-66d899939c1f",
    "showInput": false
   },
   "source": [
    "## Inference\n",
    "Inference is the process of combining _model_ with _data_ to obtain _insights_, in the form of probability distributions over values of interest. Bean Machine offers a powerful and general inference framework to enable fitting a arbitrary models to data. First lets setup our priors and instantiate our model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prior Specification\n",
    "epsilon_shape = 0.5\n",
    "epsilon_rate = 1.0\n",
    "eta_shape = 0.5\n",
    "eta_rate = 1.0\n",
    "a_1 = y_obs.mean().item()\n",
    "p_1 = 1.0\n",
    "\n",
    "# Length of our time series\n",
    "N = len(y_obs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instantiate the model and pass our length and priors\n",
    "model = LocalLevelModel(\n",
    "    N=N,\n",
    "    epsilon_shape=epsilon_shape, \n",
    "    epsilon_rate=epsilon_rate, \n",
    "    eta_shape=eta_shape,\n",
    "    eta_rate=eta_rate, \n",
    "    a_1=a_1,\n",
    "    p_1=p_1 \n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "v220qZUBY2xi",
    "originalKey": "b985154d-4f29-497e-9d61-c7f25fa3953c",
    "showInput": false
   },
   "source": [
    "Running inference consists of a few arguments:\n",
    "\n",
    "| Name | Usage\n",
    "| --- | ---\n",
    "| `queries` | A list of `@bm.random_variable` targets to fit posterior distributions for.\n",
    "| `observations` | The `Dict` of observations we built up, above.\n",
    "| `num_samples` | Number of samples to build up distributions for the values listed in `queries`.\n",
    "| `num_chains` | Number of separate inference runs to use. Multiple chains can verify inference ran correctly.\n",
    "\n",
    "The next step is to define the queries and observations. For this particular run, we're interested in infering $\\alpha$, $\\sigma_\\epsilon$, and $\\sigma_\\eta$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "queries = [model.Alpha(n) for n in range(model.N)] + [model.SigmaEpsilon(), model.SigmaEta()]\n",
    "observations = {**{model.Y(n): y_obs[n] for n in range(model.N)}}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "customInput": null,
    "originalKey": "566d8e35-0efd-4c43-a450-d4077fae2539",
    "showInput": false
   },
   "source": [
    "For this particular problem, we will use the GlobalNoUTurnSampler inference method. Bean Machine's GlobalNoUTurnSampler is the NUTS sampler you have probably used before with stan or pymc3. We have chosen to use the NUTS sampler here because it can be easily compared to other probabilistic tools."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "42abada072474f1b97720ce9fdba7ee7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Samples collected:   0%|          | 0/450 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_samples = 400 if not smoke_test else 1\n",
    "num_adaptive_samples = 50\n",
    "all_samples = bm.GlobalNoUTurnSampler(max_tree_depth=10).infer(\n",
    "    queries,\n",
    "    observations,\n",
    "    num_samples,\n",
    "    num_chains=1,\n",
    "    num_adaptive_samples=num_adaptive_samples\n",
    ")\n",
    "\n",
    "samples = all_samples.get_chain(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "alpha_samples = torch.stack([samples[model.Alpha(n)] for n in range(model.N)], dim=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2Zr_BKoLeIUv",
    "originalKey": "855d75a6-7328-4227-a378-9a815fa3c7c6",
    "showInput": false
   },
   "source": [
    "## Visualization\n",
    "We will look at the values of the samples collected for $\\alpha$ and $\\sigma_\\epsilon$. We will take the mean of samples taken over the last 10% of the chain, and compare these to our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "tail_len = num_samples // 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ppc = np.percentile(\n",
    "  alpha_samples[-tail_len:].numpy(),\n",
    "  q=[5, 50, 95],\n",
    "  axis=0\n",
    ").T\n",
    "\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.plot(y_obs, label='Observations', color='green')\n",
    "plt.plot(ppc[:, 1], label=r'Median of $\\alpha_t$', color='red')\n",
    "plt.plot(ppc[:, 0], 'b--', label=r'$5th$ Percentile')\n",
    "plt.plot(ppc[:, 2], 'b--', label=r'$95th$ Percentile')\n",
    "plt.title(r\"Observations versus $\\alpha_t$\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "code_folding": [],
    "customInput": null,
    "hidden_ranges": [],
    "originalKey": "2079490a-75e2-4275-9ccb-6cc2901b24dd",
    "showInput": false
   },
   "source": [
    "It appears inference has recovered the latent states $\\alpha$ very well, and is accurately representing $Y$ when sampled.\n",
    "\n",
    "Now we investigate chain mixing by looking at the last half of the sampling chain. This type of plot is called a *trace plot* and is useful to visualize where the sampler is proposing steps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "[plt.plot(alpha_samples[-num_samples//2:, i], label=f'Time index {i}') for i in [0, 10, 20, 30, 39]]\n",
    "plt.legend()\n",
    "plt.title(r\"Trace Plots for $\\alpha_t$\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "code_folding": [],
    "customInput": null,
    "hidden_ranges": [],
    "originalKey": "725a5271-0151-47fc-bce3-b0c5f1b26423",
    "showInput": false
   },
   "source": [
    "We see a clear downward trend for successive time indices. This matches our intuition from the \"Observations versus $\\alpha_t$\" plot as we see the original time series with a downward trend.\n",
    "\n",
    "Finally we look at the uncertainty estimates as a function of time. These are the posterior samples for both $\\sigma_\\eta$ and $\\sigma_\\epsilon$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "plt.plot(samples[model.SigmaEta()], label=r\"State Uncertainty, $\\sigma_\\eta$\")\n",
    "plt.plot(samples[model.SigmaEpsilon()], label=r\"Measurement Uncertainty, $\\sigma_\\epsilon$\")\n",
    "plt.title(\"Uncertainty\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "code_folding": [],
    "customInput": null,
    "hidden_ranges": [],
    "originalKey": "9938c1da-a286-4cd1-90e7-510e0ff365ae",
    "showInput": false
   },
   "source": [
    "We see the final noise samples are quite small, reflecting the fact that the original data did not contain alot of noise."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Q4zmF5jYgiSS",
    "originalKey": "6c34cf8d-ba3a-4950-8703-54c945d1f6cd",
    "showInput": false
   },
   "source": [
    "## Posterior Likelihood Checks\n",
    "One way to evaluate posterior samples is computing likelihoods of our posterior samples, and comparing these to the likelihood of the underlying data. Formally, we can compute the joint likelihood of posterior samples with the observations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def log_likelihood(alpha_samples, sigma_epsilon, y_obs, N):\n",
    "    ll = 0\n",
    "    for n in range(N):\n",
    "        ll += dist.Normal(alpha_samples[n], sigma_epsilon).log_prob(y_obs[n])\n",
    "    return ll\n",
    "\n",
    "\n",
    "# Iterate over the batches of samples and compute log likelihood\n",
    "lls = [\n",
    "    log_likelihood(alpha_samples, sigma_epsilon_sample, y_obs, N)\n",
    "    for alpha_samples, sigma_epsilon_sample in zip(alpha_samples, samples[model.SigmaEpsilon()])\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "plt.plot(lls, label=\"Sample\", c=\"g\")\n",
    "plt.title(\"Posterior Likelihood Check\")\n",
    "plt.ylabel(\"Log likelihood\")\n",
    "plt.xlabel(\"Iteration\")\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "code_folding": [],
    "customInput": null,
    "hidden_ranges": [],
    "originalKey": "8d46c6c1-e42b-4fdd-9132-71c187e9bd96",
    "showInput": false
   },
   "source": [
    "From the above plot, inference appears to be fitting almost perfectly. The random variables given the observed data is exceptionally high."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "code_folding": [],
    "customInput": null,
    "hidden_ranges": [],
    "originalKey": "55498d53-0235-4ade-81c8-048b8e0d6dc5",
    "showInput": false
   },
   "source": [
    "# Vectorized Model\n",
    "Now lets try a vectorized model by using a custom `Distribution`. We first extend pytorch's `torch.distributions.Distribution` into a vectorized version of a random walk. \n",
    "The key idea behind this implementation is that a [Gaussian Random Walk](https://en.wikipedia.org/wiki/Random_walk#Gaussian_random_walk) can also be expressed as a chain of iid Gaussian random variables \n",
    "that have a `cumsum` operator applied to them (thus making the \"walking\" behavior). Now with this `cumsum` operator, we will be able to sample entire chains in one step!\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.distributions import Distribution, constraints\n",
    "\n",
    "\n",
    "class GaussianRandomWalk(Distribution):\n",
    "    support = constraints.real\n",
    "\n",
    "    def __init__(\n",
    "        self,\n",
    "        loc: torch.Tensor,\n",
    "        scale: torch.Tensor,\n",
    "        num_steps: int = 1,\n",
    "        validate_args=None,\n",
    "    ) -> None:\n",
    "        assert isinstance(num_steps, int) and num_steps > 0, \"`num_steps` argument should be an positive integer.\"\n",
    "        self.loc = loc\n",
    "        self.scale = scale\n",
    "        self.num_steps = num_steps\n",
    "        batch_shape, event_shape = scale.size(), torch.Size([num_steps])\n",
    "        super(GaussianRandomWalk, self).__init__(batch_shape, event_shape, validate_args=validate_args)\n",
    "\n",
    "    def sample(self, sample_shape: List = None):\n",
    "        if not sample_shape:\n",
    "            sample_shape = []\n",
    "        assert isinstance(sample_shape, list), \"`sample_shape` argument should be a list.\"\n",
    "        shape = torch.Size(sample_shape) + self.batch_shape + self.event_shape\n",
    "        loc = torch.zeros(shape)\n",
    "        loc[..., 0] = self.loc\n",
    "        walks = dist.Normal(loc=loc, scale=1).sample()\n",
    "        return torch.cumsum(walks, axis=-1) * self.scale.unsqueeze(dim=-1)\n",
    "\n",
    "    def log_prob(self, value):\n",
    "        init_prob = dist.Normal(self.loc, self.scale).log_prob(value[..., 0])\n",
    "        scale = self.scale.unsqueeze(dim=-1)\n",
    "        step_probs = dist.Normal(value[..., :-1], scale).log_prob(value[..., 1:])\n",
    "        return init_prob + torch.sum(step_probs, axis=-1)\n",
    "\n",
    "    @property\n",
    "    def mean(self):\n",
    "        return torch.zeros(self.batch_shape + self.event_shape) + self.loc\n",
    "\n",
    "    @property\n",
    "    def variance(self):\n",
    "        return torch.broadcast_to(\n",
    "            self.scale.unsqueeze(dim=-1) ** 2 * torch.arange(1, self.num_steps + 1),\n",
    "            self.batch_shape + self.event_shape,\n",
    "        )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "code_folding": [],
    "customInput": null,
    "hidden_ranges": [],
    "originalKey": "68d451a9-8db4-42e5-b29a-1b2b5381c490",
    "showInput": false
   },
   "source": [
    "In our implementation above, we will now use the `sample()` and `log_prob()` functions to seamlessly integrate into Bean Machine!\n",
    "Specifically, we replace the `Alpha()` function with our vectorized distribution rather than recursively calling the univariate function from before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "class VectorizedLocalLevelModel:\n",
    "    def __init__(\n",
    "        self, N: int, epsilon_shape: float, epsilon_rate: float, eta_shape: float, eta_rate: float, a_1: float\n",
    "    ) -> None:\n",
    "        self.N = N\n",
    "        self.epsilon_shape = epsilon_shape\n",
    "        self.epsilon_rate = epsilon_rate\n",
    "        self.eta_shape = eta_shape\n",
    "        self.eta_rate = eta_rate\n",
    "        self.a_1 = a_1\n",
    "\n",
    "    @bm.random_variable\n",
    "    def SigmaEpsilon(self) -> RVIdentifier:\n",
    "        return dist.Gamma(self.epsilon_shape, self.epsilon_rate)\n",
    "\n",
    "    @bm.random_variable\n",
    "    def SigmaEta(self) -> RVIdentifier:\n",
    "        return dist.Gamma(self.eta_shape, self.eta_rate)\n",
    "\n",
    "    @bm.random_variable\n",
    "    def Alpha(self) -> RVIdentifier:\n",
    "        return GaussianRandomWalk(loc=self.a_1, scale=self.SigmaEta(), num_steps=self.N)  # Vectorized!\n",
    "\n",
    "    @bm.random_variable\n",
    "    def Y(self) -> RVIdentifier:\n",
    "        return dist.Normal(self.Alpha(), self.SigmaEpsilon())  # Vectorized!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "code_folding": [],
    "customInput": null,
    "hidden_ranges": [],
    "originalKey": "85250830-535d-4f55-884d-d6a2073c7a2d",
    "showInput": false
   },
   "source": [
    "We keep the rest of the workflow to be very similar to the unvectorized model so that we can directly compare the results. We complete inference, visualize our samples, and then compute some diagnostics of the fitted values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "vec_model = VectorizedLocalLevelModel(\n",
    "    N=N,\n",
    "    epsilon_shape=epsilon_shape,\n",
    "    epsilon_rate=epsilon_rate,\n",
    "    eta_shape=eta_shape,\n",
    "    eta_rate=eta_rate,\n",
    "    a_1=a_1\n",
    ")\n",
    "\n",
    "queries = [vec_model.Alpha(), vec_model.SigmaEpsilon(), vec_model.SigmaEta()]\n",
    "observations = {**{vec_model.Y(): y_obs}}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "code_folding": [],
    "customInput": null,
    "hidden_ranges": [],
    "originalKey": "b9416de6-2ffa-4b16-b992-1626dbb8bdd7",
    "showInput": false
   },
   "source": [
    "One difference is that we now fit 4 chains (serially) in order to better investigate some diagnostic metrics. You can read more about this in the $\\hat{R}$ and $ess$ cells located below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c80e3038b14a4b52b3948c3f1c5b38e2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Samples collected:   0%|          | 0/450 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "59503e34a507472f984fa167aa6ffe3f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Samples collected:   0%|          | 0/450 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3d864a02cb9a4737ac9e50cd3945c116",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Samples collected:   0%|          | 0/450 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cd4a987fb9aa483391f095c8a2ca6b1e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Samples collected:   0%|          | 0/450 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_samples = 400 if not smoke_test else 1\n",
    "vec_all_samples = bm.GlobalNoUTurnSampler(max_tree_depth=10).infer(\n",
    "    queries,\n",
    "    observations,\n",
    "    num_samples,\n",
    "    num_chains=4,\n",
    "    num_adaptive_samples=num_adaptive_samples,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The speedup is quite significant using a vectorized approach! One of the main benefits of using Bean Machine is being able to incorporate this type of fast code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([400, 40])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vec_samples = vec_all_samples.get_chain(0)\n",
    "vec_alpha_samples = vec_samples[vec_model.Alpha()]\n",
    "vec_alpha_samples.size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "code_folding": [],
    "customInput": null,
    "hidden_ranges": [],
    "originalKey": "6e77b27a-d35e-4c58-b00c-e40dc07815ed",
    "showInput": false
   },
   "source": [
    "## Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tail_len = num_samples // 10\n",
    "\n",
    "vec_ppc = np.percentile(\n",
    "  vec_alpha_samples[-tail_len:].numpy(),\n",
    "  q=[5, 50, 95],\n",
    "  axis=0\n",
    ").T\n",
    "\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.plot(y_obs, label='Observations', color='green')\n",
    "plt.plot(vec_ppc[:, 1], label=r'$\\alpha_t$', color='red')\n",
    "plt.plot(vec_ppc[:, 0], 'b--', label=r'$5th$ Percentile')\n",
    "plt.plot(vec_ppc[:, 2], 'b--', label=r'$95th$ Percentile')\n",
    "plt.title(r\"Observations versus $\\alpha_t$\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "[plt.plot(vec_alpha_samples[-num_samples//2:, i], label=f'Time index {i}') for i in [0, 10, 20, 30, 38]]\n",
    "plt.legend()\n",
    "plt.title(r\"Trace Plots for $\\alpha_t$\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(vec_samples[vec_model.SigmaEta()], label=r\"State Uncertainty, $\\sigma_\\eta$\")\n",
    "plt.plot(vec_samples[vec_model.SigmaEpsilon()], label=r\"Measurement Uncertainty, $\\sigma_\\epsilon$\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "code_folding": [],
    "customInput": null,
    "executionStartTime": 1645121951161,
    "executionStopTime": 1645121951247,
    "hidden_ranges": [],
    "id": "v2jcaDtwtSup",
    "originalKey": "2c26994a-9a70-420b-b86e-814e267476a3",
    "requestMsgId": "2c26994a-9a70-420b-b86e-814e267476a3",
    "showInput": false
   },
   "source": [
    "## Diagnostics\n",
    "Two important statistics to\n",
    "take note of are the $\\hat{R}$ (`r_hat`) and effective sample size (`ess`) values in the\n",
    "below dataframe, see [Vehtari _et al_](#references)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        vertical-align: top;\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SigmaEpsilon(&lt;__main__.VectorizedLocalLevelModel object at 0x7fee105f6f50&gt;,)</th>\n",
       "      <th>Alpha(&lt;__main__.VectorizedLocalLevelModel object at 0x7fee105f6f50&gt;,)[0]</th>\n",
       "      <th>Alpha(&lt;__main__.VectorizedLocalLevelModel object at 0x7fee105f6f50&gt;,)[1]</th>\n",
       "      <th>Alpha(&lt;__main__.VectorizedLocalLevelModel object at 0x7fee105f6f50&gt;,)[2]</th>\n",
       "      <th>Alpha(&lt;__main__.VectorizedLocalLevelModel object at 0x7fee105f6f50&gt;,)[3]</th>\n",
       "      <th>Alpha(&lt;__main__.VectorizedLocalLevelModel object at 0x7fee105f6f50&gt;,)[4]</th>\n",
       "      <th>Alpha(&lt;__main__.VectorizedLocalLevelModel object at 0x7fee105f6f50&gt;,)[5]</th>\n",
       "      <th>Alpha(&lt;__main__.VectorizedLocalLevelModel object at 0x7fee105f6f50&gt;,)[6]</th>\n",
       "      <th>Alpha(&lt;__main__.VectorizedLocalLevelModel object at 0x7fee105f6f50&gt;,)[7]</th>\n",
       "      <th>Alpha(&lt;__main__.VectorizedLocalLevelModel object at 0x7fee105f6f50&gt;,)[8]</th>\n",
       "      <th>...</th>\n",
       "      <th>Alpha(&lt;__main__.VectorizedLocalLevelModel object at 0x7fee105f6f50&gt;,)[31]</th>\n",
       "      <th>Alpha(&lt;__main__.VectorizedLocalLevelModel object at 0x7fee105f6f50&gt;,)[32]</th>\n",
       "      <th>Alpha(&lt;__main__.VectorizedLocalLevelModel object at 0x7fee105f6f50&gt;,)[33]</th>\n",
       "      <th>Alpha(&lt;__main__.VectorizedLocalLevelModel object at 0x7fee105f6f50&gt;,)[34]</th>\n",
       "      <th>Alpha(&lt;__main__.VectorizedLocalLevelModel object at 0x7fee105f6f50&gt;,)[35]</th>\n",
       "      <th>Alpha(&lt;__main__.VectorizedLocalLevelModel object at 0x7fee105f6f50&gt;,)[36]</th>\n",
       "      <th>Alpha(&lt;__main__.VectorizedLocalLevelModel object at 0x7fee105f6f50&gt;,)[37]</th>\n",
       "      <th>Alpha(&lt;__main__.VectorizedLocalLevelModel object at 0x7fee105f6f50&gt;,)[38]</th>\n",
       "      <th>Alpha(&lt;__main__.VectorizedLocalLevelModel object at 0x7fee105f6f50&gt;,)[39]</th>\n",
       "      <th>SigmaEta(&lt;__main__.VectorizedLocalLevelModel object at 0x7fee105f6f50&gt;,)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.029</td>\n",
       "      <td>13.373</td>\n",
       "      <td>13.592</td>\n",
       "      <td>13.394</td>\n",
       "      <td>13.157</td>\n",
       "      <td>13.192</td>\n",
       "      <td>13.147</td>\n",
       "      <td>13.088</td>\n",
       "      <td>13.040</td>\n",
       "      <td>13.067</td>\n",
       "      <td>...</td>\n",
       "      <td>12.954</td>\n",
       "      <td>12.911</td>\n",
       "      <td>12.863</td>\n",
       "      <td>12.856</td>\n",
       "      <td>12.847</td>\n",
       "      <td>12.755</td>\n",
       "      <td>12.846</td>\n",
       "      <td>12.829</td>\n",
       "      <td>12.832</td>\n",
       "      <td>0.113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sd</th>\n",
       "      <td>0.013</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.044</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.029</td>\n",
       "      <td>...</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hdi_3%</th>\n",
       "      <td>0.009</td>\n",
       "      <td>13.315</td>\n",
       "      <td>13.517</td>\n",
       "      <td>13.341</td>\n",
       "      <td>13.096</td>\n",
       "      <td>13.135</td>\n",
       "      <td>13.094</td>\n",
       "      <td>13.036</td>\n",
       "      <td>12.991</td>\n",
       "      <td>13.014</td>\n",
       "      <td>...</td>\n",
       "      <td>12.901</td>\n",
       "      <td>12.849</td>\n",
       "      <td>12.807</td>\n",
       "      <td>12.799</td>\n",
       "      <td>12.793</td>\n",
       "      <td>12.697</td>\n",
       "      <td>12.787</td>\n",
       "      <td>12.777</td>\n",
       "      <td>12.782</td>\n",
       "      <td>0.090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hdi_97%</th>\n",
       "      <td>0.052</td>\n",
       "      <td>13.429</td>\n",
       "      <td>13.658</td>\n",
       "      <td>13.454</td>\n",
       "      <td>13.214</td>\n",
       "      <td>13.246</td>\n",
       "      <td>13.201</td>\n",
       "      <td>13.148</td>\n",
       "      <td>13.095</td>\n",
       "      <td>13.123</td>\n",
       "      <td>...</td>\n",
       "      <td>13.008</td>\n",
       "      <td>12.962</td>\n",
       "      <td>12.915</td>\n",
       "      <td>12.908</td>\n",
       "      <td>12.899</td>\n",
       "      <td>12.812</td>\n",
       "      <td>12.897</td>\n",
       "      <td>12.884</td>\n",
       "      <td>12.892</td>\n",
       "      <td>0.140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mcse_mean</th>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mcse_sd</th>\n",
       "      <td>0.002</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ess_bulk</th>\n",
       "      <td>21.341</td>\n",
       "      <td>496.575</td>\n",
       "      <td>124.038</td>\n",
       "      <td>1102.353</td>\n",
       "      <td>248.995</td>\n",
       "      <td>1768.702</td>\n",
       "      <td>664.539</td>\n",
       "      <td>1714.346</td>\n",
       "      <td>1763.900</td>\n",
       "      <td>2192.103</td>\n",
       "      <td>...</td>\n",
       "      <td>1735.312</td>\n",
       "      <td>2262.756</td>\n",
       "      <td>1488.571</td>\n",
       "      <td>2141.008</td>\n",
       "      <td>1634.275</td>\n",
       "      <td>684.478</td>\n",
       "      <td>1478.135</td>\n",
       "      <td>1645.615</td>\n",
       "      <td>2114.612</td>\n",
       "      <td>222.529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ess_tail</th>\n",
       "      <td>51.428</td>\n",
       "      <td>278.260</td>\n",
       "      <td>138.976</td>\n",
       "      <td>335.601</td>\n",
       "      <td>225.552</td>\n",
       "      <td>315.877</td>\n",
       "      <td>600.174</td>\n",
       "      <td>835.802</td>\n",
       "      <td>732.362</td>\n",
       "      <td>511.996</td>\n",
       "      <td>...</td>\n",
       "      <td>463.851</td>\n",
       "      <td>331.508</td>\n",
       "      <td>595.205</td>\n",
       "      <td>642.630</td>\n",
       "      <td>562.712</td>\n",
       "      <td>373.118</td>\n",
       "      <td>625.599</td>\n",
       "      <td>679.928</td>\n",
       "      <td>815.075</td>\n",
       "      <td>448.152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r_hat</th>\n",
       "      <td>1.179</td>\n",
       "      <td>1.020</td>\n",
       "      <td>1.035</td>\n",
       "      <td>1.026</td>\n",
       "      <td>1.021</td>\n",
       "      <td>1.026</td>\n",
       "      <td>1.021</td>\n",
       "      <td>1.024</td>\n",
       "      <td>1.032</td>\n",
       "      <td>1.011</td>\n",
       "      <td>...</td>\n",
       "      <td>1.023</td>\n",
       "      <td>1.042</td>\n",
       "      <td>1.024</td>\n",
       "      <td>1.022</td>\n",
       "      <td>1.022</td>\n",
       "      <td>1.012</td>\n",
       "      <td>1.043</td>\n",
       "      <td>1.017</td>\n",
       "      <td>1.027</td>\n",
       "      <td>1.018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           SigmaEpsilon(<__main__.VectorizedLocalLevelModel object at 0x7fee105f6f50>,)  \\\n",
       "mean                                                   0.029                              \n",
       "sd                                                     0.013                              \n",
       "hdi_3%                                                 0.009                              \n",
       "hdi_97%                                                0.052                              \n",
       "mcse_mean                                              0.002                              \n",
       "mcse_sd                                                0.002                              \n",
       "ess_bulk                                              21.341                              \n",
       "ess_tail                                              51.428                              \n",
       "r_hat                                                  1.179                              \n",
       "\n",
       "           Alpha(<__main__.VectorizedLocalLevelModel object at 0x7fee105f6f50>,)[0]  \\\n",
       "mean                                                  13.373                          \n",
       "sd                                                     0.032                          \n",
       "hdi_3%                                                13.315                          \n",
       "hdi_97%                                               13.429                          \n",
       "mcse_mean                                              0.002                          \n",
       "mcse_sd                                                0.001                          \n",
       "ess_bulk                                             496.575                          \n",
       "ess_tail                                             278.260                          \n",
       "r_hat                                                  1.020                          \n",
       "\n",
       "           Alpha(<__main__.VectorizedLocalLevelModel object at 0x7fee105f6f50>,)[1]  \\\n",
       "mean                                                  13.592                          \n",
       "sd                                                     0.044                          \n",
       "hdi_3%                                                13.517                          \n",
       "hdi_97%                                               13.658                          \n",
       "mcse_mean                                              0.005                          \n",
       "mcse_sd                                                0.003                          \n",
       "ess_bulk                                             124.038                          \n",
       "ess_tail                                             138.976                          \n",
       "r_hat                                                  1.035                          \n",
       "\n",
       "           Alpha(<__main__.VectorizedLocalLevelModel object at 0x7fee105f6f50>,)[2]  \\\n",
       "mean                                                  13.394                          \n",
       "sd                                                     0.030                          \n",
       "hdi_3%                                                13.341                          \n",
       "hdi_97%                                               13.454                          \n",
       "mcse_mean                                              0.001                          \n",
       "mcse_sd                                                0.001                          \n",
       "ess_bulk                                            1102.353                          \n",
       "ess_tail                                             335.601                          \n",
       "r_hat                                                  1.026                          \n",
       "\n",
       "           Alpha(<__main__.VectorizedLocalLevelModel object at 0x7fee105f6f50>,)[3]  \\\n",
       "mean                                                  13.157                          \n",
       "sd                                                     0.032                          \n",
       "hdi_3%                                                13.096                          \n",
       "hdi_97%                                               13.214                          \n",
       "mcse_mean                                              0.002                          \n",
       "mcse_sd                                                0.002                          \n",
       "ess_bulk                                             248.995                          \n",
       "ess_tail                                             225.552                          \n",
       "r_hat                                                  1.021                          \n",
       "\n",
       "           Alpha(<__main__.VectorizedLocalLevelModel object at 0x7fee105f6f50>,)[4]  \\\n",
       "mean                                                  13.192                          \n",
       "sd                                                     0.030                          \n",
       "hdi_3%                                                13.135                          \n",
       "hdi_97%                                               13.246                          \n",
       "mcse_mean                                              0.001                          \n",
       "mcse_sd                                                0.001                          \n",
       "ess_bulk                                            1768.702                          \n",
       "ess_tail                                             315.877                          \n",
       "r_hat                                                  1.026                          \n",
       "\n",
       "           Alpha(<__main__.VectorizedLocalLevelModel object at 0x7fee105f6f50>,)[5]  \\\n",
       "mean                                                  13.147                          \n",
       "sd                                                     0.028                          \n",
       "hdi_3%                                                13.094                          \n",
       "hdi_97%                                               13.201                          \n",
       "mcse_mean                                              0.001                          \n",
       "mcse_sd                                                0.001                          \n",
       "ess_bulk                                             664.539                          \n",
       "ess_tail                                             600.174                          \n",
       "r_hat                                                  1.021                          \n",
       "\n",
       "           Alpha(<__main__.VectorizedLocalLevelModel object at 0x7fee105f6f50>,)[6]  \\\n",
       "mean                                                  13.088                          \n",
       "sd                                                     0.029                          \n",
       "hdi_3%                                                13.036                          \n",
       "hdi_97%                                               13.148                          \n",
       "mcse_mean                                              0.001                          \n",
       "mcse_sd                                                0.001                          \n",
       "ess_bulk                                            1714.346                          \n",
       "ess_tail                                             835.802                          \n",
       "r_hat                                                  1.024                          \n",
       "\n",
       "           Alpha(<__main__.VectorizedLocalLevelModel object at 0x7fee105f6f50>,)[7]  \\\n",
       "mean                                                  13.040                          \n",
       "sd                                                     0.027                          \n",
       "hdi_3%                                                12.991                          \n",
       "hdi_97%                                               13.095                          \n",
       "mcse_mean                                              0.001                          \n",
       "mcse_sd                                                0.000                          \n",
       "ess_bulk                                            1763.900                          \n",
       "ess_tail                                             732.362                          \n",
       "r_hat                                                  1.032                          \n",
       "\n",
       "           Alpha(<__main__.VectorizedLocalLevelModel object at 0x7fee105f6f50>,)[8]  \\\n",
       "mean                                                  13.067                          \n",
       "sd                                                     0.029                          \n",
       "hdi_3%                                                13.014                          \n",
       "hdi_97%                                               13.123                          \n",
       "mcse_mean                                              0.001                          \n",
       "mcse_sd                                                0.000                          \n",
       "ess_bulk                                            2192.103                          \n",
       "ess_tail                                             511.996                          \n",
       "r_hat                                                  1.011                          \n",
       "\n",
       "           ...  \\\n",
       "mean       ...   \n",
       "sd         ...   \n",
       "hdi_3%     ...   \n",
       "hdi_97%    ...   \n",
       "mcse_mean  ...   \n",
       "mcse_sd    ...   \n",
       "ess_bulk   ...   \n",
       "ess_tail   ...   \n",
       "r_hat      ...   \n",
       "\n",
       "           Alpha(<__main__.VectorizedLocalLevelModel object at 0x7fee105f6f50>,)[31]  \\\n",
       "mean                                                  12.954                           \n",
       "sd                                                     0.029                           \n",
       "hdi_3%                                                12.901                           \n",
       "hdi_97%                                               13.008                           \n",
       "mcse_mean                                              0.001                           \n",
       "mcse_sd                                                0.000                           \n",
       "ess_bulk                                            1735.312                           \n",
       "ess_tail                                             463.851                           \n",
       "r_hat                                                  1.023                           \n",
       "\n",
       "           Alpha(<__main__.VectorizedLocalLevelModel object at 0x7fee105f6f50>,)[32]  \\\n",
       "mean                                                  12.911                           \n",
       "sd                                                     0.028                           \n",
       "hdi_3%                                                12.849                           \n",
       "hdi_97%                                               12.962                           \n",
       "mcse_mean                                              0.001                           \n",
       "mcse_sd                                                0.000                           \n",
       "ess_bulk                                            2262.756                           \n",
       "ess_tail                                             331.508                           \n",
       "r_hat                                                  1.042                           \n",
       "\n",
       "           Alpha(<__main__.VectorizedLocalLevelModel object at 0x7fee105f6f50>,)[33]  \\\n",
       "mean                                                  12.863                           \n",
       "sd                                                     0.028                           \n",
       "hdi_3%                                                12.807                           \n",
       "hdi_97%                                               12.915                           \n",
       "mcse_mean                                              0.001                           \n",
       "mcse_sd                                                0.001                           \n",
       "ess_bulk                                            1488.571                           \n",
       "ess_tail                                             595.205                           \n",
       "r_hat                                                  1.024                           \n",
       "\n",
       "           Alpha(<__main__.VectorizedLocalLevelModel object at 0x7fee105f6f50>,)[34]  \\\n",
       "mean                                                  12.856                           \n",
       "sd                                                     0.029                           \n",
       "hdi_3%                                                12.799                           \n",
       "hdi_97%                                               12.908                           \n",
       "mcse_mean                                              0.001                           \n",
       "mcse_sd                                                0.000                           \n",
       "ess_bulk                                            2141.008                           \n",
       "ess_tail                                             642.630                           \n",
       "r_hat                                                  1.022                           \n",
       "\n",
       "           Alpha(<__main__.VectorizedLocalLevelModel object at 0x7fee105f6f50>,)[35]  \\\n",
       "mean                                                  12.847                           \n",
       "sd                                                     0.027                           \n",
       "hdi_3%                                                12.793                           \n",
       "hdi_97%                                               12.899                           \n",
       "mcse_mean                                              0.001                           \n",
       "mcse_sd                                                0.000                           \n",
       "ess_bulk                                            1634.275                           \n",
       "ess_tail                                             562.712                           \n",
       "r_hat                                                  1.022                           \n",
       "\n",
       "           Alpha(<__main__.VectorizedLocalLevelModel object at 0x7fee105f6f50>,)[36]  \\\n",
       "mean                                                  12.755                           \n",
       "sd                                                     0.031                           \n",
       "hdi_3%                                                12.697                           \n",
       "hdi_97%                                               12.812                           \n",
       "mcse_mean                                              0.001                           \n",
       "mcse_sd                                                0.001                           \n",
       "ess_bulk                                             684.478                           \n",
       "ess_tail                                             373.118                           \n",
       "r_hat                                                  1.012                           \n",
       "\n",
       "           Alpha(<__main__.VectorizedLocalLevelModel object at 0x7fee105f6f50>,)[37]  \\\n",
       "mean                                                  12.846                           \n",
       "sd                                                     0.029                           \n",
       "hdi_3%                                                12.787                           \n",
       "hdi_97%                                               12.897                           \n",
       "mcse_mean                                              0.001                           \n",
       "mcse_sd                                                0.001                           \n",
       "ess_bulk                                            1478.135                           \n",
       "ess_tail                                             625.599                           \n",
       "r_hat                                                  1.043                           \n",
       "\n",
       "           Alpha(<__main__.VectorizedLocalLevelModel object at 0x7fee105f6f50>,)[38]  \\\n",
       "mean                                                  12.829                           \n",
       "sd                                                     0.029                           \n",
       "hdi_3%                                                12.777                           \n",
       "hdi_97%                                               12.884                           \n",
       "mcse_mean                                              0.001                           \n",
       "mcse_sd                                                0.001                           \n",
       "ess_bulk                                            1645.615                           \n",
       "ess_tail                                             679.928                           \n",
       "r_hat                                                  1.017                           \n",
       "\n",
       "           Alpha(<__main__.VectorizedLocalLevelModel object at 0x7fee105f6f50>,)[39]  \\\n",
       "mean                                                  12.832                           \n",
       "sd                                                     0.029                           \n",
       "hdi_3%                                                12.782                           \n",
       "hdi_97%                                               12.892                           \n",
       "mcse_mean                                              0.001                           \n",
       "mcse_sd                                                0.000                           \n",
       "ess_bulk                                            2114.612                           \n",
       "ess_tail                                             815.075                           \n",
       "r_hat                                                  1.027                           \n",
       "\n",
       "           SigmaEta(<__main__.VectorizedLocalLevelModel object at 0x7fee105f6f50>,)  \n",
       "mean                                                   0.113                         \n",
       "sd                                                     0.014                         \n",
       "hdi_3%                                                 0.090                         \n",
       "hdi_97%                                                0.140                         \n",
       "mcse_mean                                              0.001                         \n",
       "mcse_sd                                                0.001                         \n",
       "ess_bulk                                             222.529                         \n",
       "ess_tail                                             448.152                         \n",
       "r_hat                                                  1.018                         \n",
       "\n",
       "[9 rows x 42 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import arviz as az\n",
    "summary_df = az.summary(vec_all_samples.to_xarray(), round_to=3)\n",
    "summary_df.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "code_folding": [],
    "customInput": null,
    "hidden_ranges": [],
    "originalKey": "abedcecc-aec0-474d-bcbd-3e0ae8557fdf",
    "showInput": false
   },
   "source": [
    "### $\\hat{R}$ Diagnostic\n",
    "\n",
    "$\\hat{R}$ is a diagnostic tool that measures the between- and within-chain variances. It\n",
    "is a test that indicates a lack of convergence by comparing the variance between\n",
    "multiple chains to the variance within each chain. If the parameters are successfully\n",
    "exploring the full space for each chain, then $\\hat{R}\\approx 1$, since the\n",
    "between-chain and within-chain variance should be equal. $\\hat{R}$ is calculated as\n",
    "\n",
    "$$\n",
    "\\hat{R}=\\frac{\\hat{V}}{W}\n",
    "$$\n",
    "\n",
    "where $W$ is the within-chain variance and $\\hat{V}$ is the posterior variance estimate\n",
    "for the pooled rank-traces. The take-away here is that $\\hat{R}$ converges towards 1\n",
    "when each of the Markov chains approaches perfect adaptation to the true posterior\n",
    "distribution. We do not recommend using inference results if $\\hat{R}>1.01$. More\n",
    "information about $\\hat{R}$ can be found in the [Vehtari _et al_](#references) paper."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "code_folding": [],
    "customInput": null,
    "hidden_ranges": [],
    "originalKey": "16cc9d57-ecb7-4660-b21b-08f6b6f3336a",
    "showInput": false
   },
   "source": [
    "### Effective sample size $ess$ diagnostic\n",
    "\n",
    "MCMC samplers do not draw independent samples from the target distribution, which means\n",
    "that our samples are correlated. In an ideal situation all samples would be independent,\n",
    "but we do not have that luxury. We can, however, measure the number of _effectively\n",
    "independent_ samples we draw, which is called the effective sample size. You can read\n",
    "more about how this value is calculated in the [Vehtari _et al_](#references) paper,\n",
    "briefly it is a measure that combines information from the $\\hat{R}$ value with the\n",
    "autocorrelation estimates within the chains. There are many ways to estimate effective\n",
    "samples sizes, however, we will be using the method defined in the [Vehtari _et\n",
    "al_](#references) paper.\n",
    "\n",
    "The rule of thumb for `ess_bulk` is for this value to be greater than 100 per chain on\n",
    "average. Since we ran four chains, we need `ess_bulk` to be greater than 400 for each\n",
    "parameter. The `ess_tail` is an estimate for effectively independent samples considering\n",
    "the more extreme values of the posterior. This is not the number of samples that landed\n",
    "in the tails of the posterior. It is a measure of the number of effectively independent\n",
    "samples if we sampled the tails of the posterior. The rule of thumb for this value is\n",
    "also to be greater than 100 per chain on average.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "code_folding": [],
    "customInput": null,
    "hidden_ranges": [],
    "originalKey": "5ae271c6-483d-494c-adac-83d796e11613",
    "showInput": false
   },
   "source": [
    "# Heteroscedastic Model\n",
    "In the previous sections, one core assumption made about the data generation process is that $\\epsilon_t \\stackrel{iid}{\\sim} \\text{Normal}(0, \\sigma_\\epsilon^2)$,\n",
    "meaning that all of the noise in our measurement equation was sampled the same way, from the same distribution.\n",
    "In the Statistics literature, this is known as *homoscedastic* noise. Instead, we could postulate a second latent random variable, $\\beta_t$ that would capture\n",
    "some of the serial correlation in the *noise* over time (as opposed to the level). This is referred to as a *heteroscedastic* noise. That is, we could say $\\beta = g(\\beta_{t - 1})$. \n",
    "One simple choice for $g(\\cdot)$ is the same choice we made for $\\alpha_t$, that is:\n",
    "$$\\beta_{t + 1} = \\beta_{t} + \\epsilon_{t}$$\n",
    "Where the definition of $\\epsilon_t$ doesn't change (or its associated prior, $\\sigma_\\epsilon$). Now, our model looks like this:\n",
    "* $\\alpha_{t+1} | \\alpha_t \\sim \\text{Normal}(\\alpha_{t}, \\sigma_\\eta^2)$\n",
    "* $\\beta_{t+1} | \\beta_t \\sim \\text{Normal}(\\beta_{t}, \\sigma_\\epsilon^2)$\n",
    "* $y_t | \\alpha_t \\sim \\text{Normal}(\\alpha_t, f(\\beta_t^2))$\n",
    "\n",
    "So that as the process goes on, the noise can now \"walk\" just like our level from before. The last detail to attend to\n",
    "is the function $f(\\cdot)$. Since the `Normal` distribution's `scale` argument lives in $\\mathbb R^+$, we need to apply a transformation\n",
    "to $\\beta_t$ for the `scale` argument to be valid. One nice candidate is [torch.nn.Softplus()](https://pytorch.org/docs/stable/generated/torch.nn.Softplus.html)\n",
    "which is a smooth approximation to the `Relu` function. We will use the `@bm.functional` wrapper to accomplish this deterministic mapping, \n",
    "creating an intermediate random variable called `Scale` in the process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "class HeteroLocalLevelModel:\n",
    "    def __init__(\n",
    "        self,\n",
    "        N: int,\n",
    "        epsilon_shape: float,\n",
    "        epsilon_rate: float,\n",
    "        eta_shape: float,\n",
    "        eta_rate: float,\n",
    "        a_1: float,\n",
    "    ) -> None:\n",
    "        self.N = N\n",
    "        self.epsilon_shape = epsilon_shape\n",
    "        self.epsilon_rate = epsilon_rate\n",
    "        self.eta_shape = eta_shape\n",
    "        self.eta_rate = eta_rate\n",
    "        self.a_1 = a_1\n",
    "        self.p_1 = p_1\n",
    "\n",
    "    @bm.random_variable\n",
    "    def SigmaEpsilon(self) -> RVIdentifier:\n",
    "        return dist.Gamma(self.epsilon_shape, self.epsilon_rate)\n",
    "\n",
    "    @bm.random_variable\n",
    "    def SigmaEta(self) -> RVIdentifier:\n",
    "        return dist.Gamma(self.eta_shape, self.eta_rate)\n",
    "\n",
    "    @bm.random_variable\n",
    "    def Alpha(self) -> RVIdentifier:\n",
    "        return GaussianRandomWalk(loc=self.a_1, scale=self.SigmaEta(), num_steps=self.N)  # Vectorized!\n",
    "\n",
    "    @bm.random_variable\n",
    "    def Beta(self) -> RVIdentifier:\n",
    "        return GaussianRandomWalk(loc=torch.Tensor([0.0]), scale=self.SigmaEpsilon(), num_steps=self.N)  # Vectorized!\n",
    "\n",
    "    @bm.functional\n",
    "    def Scale(self) -> RVIdentifier:\n",
    "        return torch.nn.Softplus()(self.Beta())  # Transform Beta to a valid scale argument\n",
    "\n",
    "    @bm.random_variable\n",
    "    def Y(self) -> RVIdentifier:\n",
    "        return dist.Normal(self.Alpha(), self.Scale())  # Vectorized!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e0c51d39bede41ba97649fd1ca4c65db",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Samples collected:   0%|          | 0/550 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hetero_model = HeteroLocalLevelModel(\n",
    "    N=N,\n",
    "    epsilon_shape=epsilon_shape,\n",
    "    epsilon_rate=epsilon_rate,\n",
    "    eta_shape=eta_shape,\n",
    "    eta_rate=eta_rate,\n",
    "    a_1=a_1\n",
    ")\n",
    "\n",
    "queries = [hetero_model.Alpha(), hetero_model.Scale()]\n",
    "observations = {**{hetero_model.Y(): y_obs}}\n",
    "num_samples = 400 if not smoke_test else 1\n",
    "hetero_all_samples = bm.GlobalNoUTurnSampler().infer(\n",
    "    queries, observations, num_samples, num_chains=1, num_adaptive_samples=num_adaptive_samples * 3\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "hetero_samples = hetero_all_samples.get_chain(0)\n",
    "hetero_alpha_samples = hetero_samples[hetero_model.Alpha()]\n",
    "hetero_scale_samples = hetero_samples[hetero_model.Scale()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization\n",
    "One last note before visualization, we are now computing uncertainty of both our level ($\\alpha_t$),\n",
    "our noise ($\\beta_t$), and our observations $y_t$. These \"types\" of uncertainty can be broadly cast into two \n",
    "seperate categories: **aleatoric** uncertainty and **epistimic** uncertainty.\n",
    "\n",
    "A quick read on the topic can be found \n",
    "[here](https://en.wikipedia.org/wiki/Uncertainty_quantification) but the gist is that **aleatoric** is the\n",
    "data uncertainty, and measures the inherent randomness in the observed data. This type of uncertainty would be appropriate\n",
    "for forming *prediction* intervals for new data. **Epistimic** uncertainty on the other hand, measures the uncertainty in a *parameter* estimate.\n",
    "So here, we are estimating the conditional mean ($\\alpha_t$) and conditional variance ($f(\\beta_t)^2$). This type of \n",
    "uncertainty is useful for forming *credible* intervals. We first explore **aleatoric** uncertainty and then see how it is impacted by also considering **epistemic** uncertainty."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Point estimates of Alpha and Beta\n",
    "loc = hetero_alpha_samples[-tail_len:].mean(0)\n",
    "scale = hetero_scale_samples[-tail_len:].mean(0)\n",
    "aleatoric = dist.Normal(loc, scale)\n",
    "quantiles = aleatoric.icdf(torch.Tensor([0.05, 0.95]).unsqueeze(-1))\n",
    "\n",
    "# Form prediction interval with the point estimates\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(y_obs, label=\"Observations\", color=\"green\")\n",
    "plt.plot(loc, \"r--\", label=r\"Mean of $\\alpha_t$\")\n",
    "plt.fill_between(torch.arange(N), quantiles[0, ...], quantiles[1, ...], alpha=0.5, label=\"Prediction Interval\")\n",
    "plt.title(r\"Aleatoric Uncertainty: Observations versus $\\alpha_t$\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As seen from the plot, the mean of $\\alpha_t$ appears much smoother than before. The reason is because by fitting both the *location* and *scale* of $\\text{Normal}(\\alpha_t, f(\\beta_t))$, we enable a more flexible fit to the data (contrast this from before when `SigmaEpsilon()` was a single parameter that was supposed to estimate the noise for the entire sequence!). For example, when the data becomes more noisy, our model can simply widen $\\beta_t$ without overcorrecting $\\alpha_t$ which ensures smoother connections with later points. Thus, the model is able to more accurately detect a *latent* trend while still learning an evolving (*heteroskedastic*) noise level. This is an example of learning the **aleatoric** uncertainty (inherent randomness), which we display with a *prediction* interval. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we show the other type of uncertainty, **epistemic** uncertainty."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "epistemic_alpha = np.quantile(hetero_alpha_samples[-tail_len:].detach().numpy(), [0.05, 0.95], axis=0)\n",
    "\n",
    "# CI for Beta\n",
    "plt.figure(figsize=(16, 10))\n",
    "plt.fill_between(\n",
    "    torch.arange(N), \n",
    "    np.quantile((hetero_alpha_samples[-tail_len:] - hetero_scale_samples[-tail_len:]).detach().numpy(), 0.05, 0),\n",
    "    np.quantile((hetero_alpha_samples[-tail_len:] + hetero_scale_samples[-tail_len:]).detach().numpy(), 0.95, 0), \n",
    "    alpha=0.5,\n",
    "    label=r\"95% Credible Interval for $\\beta_t \\pm \\alpha_t$\"\n",
    ")\n",
    "# Beta samples\n",
    "plt.plot(hetero_alpha_samples[-tail_len:].T - hetero_scale_samples[-tail_len:].T, 'r--', linewidth=0.1)\n",
    "plt.plot(hetero_alpha_samples[-tail_len:].T + hetero_scale_samples[-tail_len:].T, 'r--', linewidth=0.1)\n",
    "plt.plot(hetero_alpha_samples[-tail_len:].T[0] + hetero_scale_samples[-tail_len:].T[0], 'r--', label=r'Samples of $\\beta_t \\pm \\alpha_t$', linewidth=0.1)\n",
    "# CI for Alpha\n",
    "plt.fill_between(\n",
    "    torch.arange(N), \n",
    "    epistemic_alpha[0, ...], \n",
    "    epistemic_alpha[1, ...], \n",
    "    alpha=0.5, \n",
    "    color='red', \n",
    "    label=r\"95% Credible Interval for $\\alpha_t$\"\n",
    ")\n",
    "# Samples for Alpha\n",
    "plt.plot(hetero_alpha_samples[-tail_len:].T, 'b--', linewidth=0.3)\n",
    "plt.plot(hetero_alpha_samples[-tail_len:].T[0, ...], 'b--', label=r'Samples of $\\alpha_t$', linewidth=0.3)\n",
    "plt.plot(y_obs, label=\"Observations\", color=\"green\", linewidth=3)\n",
    "plt.title(r\"Epistemic Uncertainty: Observations versus $\\alpha_t$\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we showed how there was uncertainty in where the data will be observed next (**aleatoric**) which we encoded with a distribution $\\text{Normal}(\\alpha_t, \\beta_t)$ made up of point estimates of parameters `Alpha` and `Beta`. But remember, these parameters are themselves estimated from the data and have additional uncertainty from the parameter estimation process! This is the **epistemic** uncertainty. Rather than show the point estimates, above we show a 95% credible interval for both $\\alpha_t$ and $\\beta_t$ at each time index. Note that $\\beta_t$'s uncertainty is superimposed (added and subtracted) on top of $\\alpha_t$ since it is measured in different units. It is now clear that by integrating in the *epistemic* uncertainty into our parameter estimates for the *aleatoric* uncertainty, the corresponding prediction intervals would be a great deal wider."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In either case, by using Bean Machine we can handle epistemic *and* aleatoric uncertainty quite easily with only about 1-2 lines of extra code! "
   ]
  }
 ],
 "metadata": {
  "language_info": {
   "name": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
